Method and apparatus for image analysis of a gabor-wavelet transformed image using a neural network

Abstract

The present invention may be embodied in a method, and in a related apparatus, for classifying a feature in an image frame. In the method, an original image frame having an array of pixels is transformed using Gabor-wavelet transformations to generate a transformed image frame. Each pixel of the transformed image is associated with a respective pixel of the original image frame and is represented by a predetermined number of wavelet component values. A pixel of the transformed image frame associated with the feature is selected for analysis. A neural network is provided that has an output and a predetermined number of inputs. Each input of the neural network is associated with a respective wavelet component value of the selected pixel. The neural network classifies the local feature based on the wavelet component values, and indicates a class of the feature at an output of the neural network.

Description

BACKGROUND OF THE INVENTION[0001]The present invention relates to image analysis, and more particularly, to feature classification and object analysis in an image frame.[0002]Robust image analysis is able to classify objects or features in an image frame under a variety of lighting conditions, backgrounds, orientations, etc. However, existing image analysis techniques may not be sufficiently robust to achieve desired results.[0003]Accordingly, there exists a significant need for improved and robust image analysis. The present invention satisfies this need.SUMMARY OF THE INVENTION[0004]The present invention may be embodied in a method, and in a related apparatus, for classifying a feature in an image frame. In the method, an original image frame having an array of pixels is transformed using wavelet transformations to generate a transformed image frame having an array of pixels. Each pixel of the transformed image is associated with a respective pixel of the original image frame and ...

AI Technical Summary

Benefits of technology

[0005]In more detailed features of the present invention, the wavelet transformations may use Gabor wavelets and each wavelet component value may be generated based on a Gabor wavelet having a particular orientation and frequency. The predetermined number of wavelet component values and the predetermined number of neural network inputs may be 12. Also, the wavelet component values may be magnitudes of complex numbers.

Problems solved by technology

However, existing image analysis techniques may not be sufficiently robust to achieve desired results.

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